Ali Salman, Hussain A.Ali, & Alaa Jameel

Dr. Shaker Haji & Dr. Mohamed Bin Shams

Feb 2-4, 2014 – Muscat, Sultanate of Oman

2

Feb 21, 2010

Visitors Center Hall:10 x 60 W light bulbs + a 2 ton A/C

Total ~ 3 kW

20 x 200 Wp = 4 kWp

1.7 kW @ 12 m/scut in: 3 m/s

4 x 260Ah/12V (48 V DC System - Series)

2 x 60 NL/h H2 Generator

(water electrolyzers)

6 x 500 NL @ 10 bar H2 M-H

Canisters1.2 kW Nexa FC Stack

2 Renewable Energy Sources

Public Grid

Bapco’s Green Energy Station Configuration.5

Simplified Power Management Strategy [1]6

7

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12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM

Tem

pe

ratu

re [

oC

] o

r W

ind

Ve

loci

ty [

m/s

]

Po

we

r [W

/m2]

Date & Time

Environmental Conditions

Solar Irradiation

Ambient Temperature

Module Temperature

Wind Velocity

24/06/2010 25/06/2010

Courtesy of Bapco

Climate Related Data

8

Courtesy of Bapco

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12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM

Bat

tery

SO

C [

%]

or

H2

Pre

ssu

re [b

ar]

Po

we

r [W

]

Date & Time

AC Laod P [W]

PV P [W]

WT P [W]

FC P [W]

Bat. SOC [%]

H2 Pressure

24/06/2010 25/06/2010

Energy Related Data

9

What does the performance of a Hybrid

Renewable Energy System depend on?

10

Performance

Climate Conditions

Energy Generators

Power Demand

Energy Storage

(1) To analyze, model, & forecast

meteorological data (driving forces)

(2) To analyze the energy data from the hybrid

RE system

Ultimate Goal: Optimize the performance of the RE

system (e.g. maximizing CO2 saving)

11

Data collection (1st May 2010 – 30th April 2011)

Bapco’s Green Energy Station Data.

Objective 1: To analyze, model, & forecast meteorological data Time Series Analysis.

• Why do we use Time Series Analysis?

- Data are correlated in time

Time Series Analysis:

AR: Auto Regressive

MA: Moving Average

ARMA

𝒚𝒕 = 𝜹 + Ф𝟏𝒚𝒕−𝟏 + Ф𝟐𝒚𝒕−𝟐 + ⋯+ Ф𝒑𝒚𝒕−𝒑 + 𝜺𝒕 −𝜽𝟏𝜺𝒕−𝟏 − 𝜽𝟐𝜺𝒕−𝟐 − ⋯−𝜽𝒒𝜺𝒕−𝒒

15

Time Series Analysis:

. Data should be stationary.

StationaryConst. Mean

Const. Variance

Time Series Analysis :

• What if I don’t have stationary data?!• Transformations: ARIMA (p,I,q)

– (const. mean)

– (const. variance)

Differencing Transformation

Box – Cox Transformation

Mean Stabilization

Differencing Transformation [3]

BOX-COX Transformation [3]

Variance Stabilization

7. Other Statistical Analysis

6. Validating Forecasting Accuracy

5. Forecasting

4. Validating Models (Residual Analysis)

3. Parameter Estimation (Least Square Error)

2. Stationarizing data (if required)

1. Estimating Missing Points

Time Series Analysis (meteorological data)

Energy Data Analysis

1. Efficiency Calculations: various systems

2. Energy Calculations: Load, RE, Storage & PG

3. Calculations of CO2 Savings & Emissions

4. Feasibility Calculations: PV & WT

0

1

1

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2

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3

4

4

5

Spee

d, m

/s

Actual wind speed Modeled wind speed

0

1

1

2

2

3

3

4

4

5

Spee

d, m

/s

Actual Data Forcasted Data UCL LCL

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1

1

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2

3

3

4

4

5

Spee

d, m

/s

Actual wind speed

• Average daily wind speed: ARIMA (1,0,0):

𝒚𝒕 = 𝟎. 𝟏𝟕𝟗𝟔𝟑𝟕 + 𝟎.𝟔𝟒𝟑𝟏𝟔 𝒚𝒕−𝟏 + 𝜺𝒕

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

Trans. wind velocity

𝒚 = 𝒙 𝟎.𝟑𝟑𝟏𝟔𝟔𝟖−𝟏

𝟎.𝟑𝟑𝟏𝟔𝟔𝟖

0

100

200

300

400

500

600

Rad

iati

on

W/m

2

UCL LCL Forecasted Data Actual Data

0

100

200

300

400

500

600

Rad

aiti

on

W/m

2

Actual Solar Radiation Modeled Solar Radiation

• Average daily solar radiation: ARIMA (1,0,0):

𝒚𝒕 = 𝟖𝟔𝟐𝟏𝟔𝟔𝟗 + 𝟎.𝟓𝟑𝟓𝟐𝟐 𝒚𝒕−𝟏 + 𝜺𝒕

15.00

20.00

25.00

30.00

35.00

40.00

Tem

per

atu

re,

C

Actual Data Forecasted Data UCL LCL

5

10

15

20

25

30

35

40

45

Tem

per

atu

re,

C

Actual Ambient Temperature Modeled Ambient Temperature

• Average daily ambient temperature: ARIMA (0,1,2):

𝒚𝒕 = −𝟎. 𝟎𝟖𝟕 + 𝒚𝒕−𝟏 + 𝟎.𝟐𝟏𝟑𝜺𝒕−𝟏 + 𝟎.𝟐𝟔𝟔𝜺𝒕−𝟐 + 𝜺𝒕

10

15

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25

30

35

40

45

50

Tem

per

atu

re, °

C

Actual Module Temperature Modeled Module Temperature

20

25

30

35

40

45

50

Tem

per

atu

re, °

C

Actual Data Forcasted Data UCL LCL

• Average daily solar module temperature: ARIMA (0,1,1):

𝒚𝒕 = −𝟎. 𝟎𝟎𝟒𝟒 + 𝒚𝒕−𝟏 + 𝟎.𝟒𝟏𝟒𝟐𝟏 𝜺𝒕−𝟏 + 𝜺𝒕

0

500

1,000

1,500

2,000

2,500

3,000

3,500

Ener

gy, M

J

Monthly load of the station

Load of the station, MJ

5.14 GJ30%

00%

10.7 GJ90%

1.25 GJ10%

11.95 GJ70%

Annual and systems contributions to load demand

Grid

Renewable Energy

Solar system

Wind Energy

0%

20%

40%

60%

80%

100%

Mo

nth

ly C

on

trib

uti

on

, %

Monthly systems contributions to load demand

Monthly Contribution of Renewable Sources, % Monthly Contribution of public Grid, %

0

100

200

300

400

500

600

700

800

900

1000

Ener

gy, M

J

Monthly energy out from storage system

Storage Energy Out from Batteries, MJ Storage Energy Out from Fuel Cell, MJ

4268 MJ95%

236 MJ5%

Annual energy out from storage system

Annual StorageEnergy Out fromBatteries

Annual StorageEnergy Out fromFuel Cell

• CO2 Savings:

0

50

100

150

200

250

CO

2, k

g

Estimated CO2 Savings, kg CO2 Emitted, kg

Annual Emitted CO2 , kgAnnual Saved CO2 , kg

6211,429

• Units efficiencies:

(a) Average monthly efficiency

(b) Average instantaneous efficiency for October and February

Equipment Efficiency, % Maximum MinimumStandarddeviation

Reference

Wind Turbine 57.64% (a) 74.34% 37.29% 9.74% < 59% (Bitz) [4]

Solar Panel 7.00% (a) 11.56% 3.15% 2.46% < 15% max [5]

Fuel Cell 37.7% (b) 37.75% 37.65% - < 40-60% [6]

• Effectiveness, Payback period, energy cost of Wind & Solar systems:

Equip.Cost, $/W

Life-time, years

Rated Power,

kW

Avg. Meas. Power,

kW

Effectiveness, %

PBP, years Energy Cost, $/kWh

(fils/kWh)Comm. Resid.

Wind Turbine

2.4 20 1.7 0.0482 2.83 276 1349 1.18 (442)

Solar Panel

4.2 20 4 1.1161 25.76 133.5 680 0.59 (223)

* Effectiveness = Avg. Measured Power / Rated Power

– Saving the Environment!

– Building Capacities & Gaining Experiences

– Opportunities for Carbon Credit & Personal Carbon Trading

– Government Savings:

• on building & operating new power plants

• on the natural gas (NG) sold to local power plants at subsidized price.

• on the electricity purchased from private power plants

– Government Opportunities:

• in selling the saved NG at market price

• in using the saved NG in petrochemical industries

– The following models were found satisfactory in modeling:• ARIMA(1,0,0): Wind speed• ARIMA(1,0,0): Solar radiation• ARIMA(0,1,2): Ambient temperature • ARIMA(0,1,1): Solar module’s temperature data

– The station load was met by renewable energy (70%) and public grid (30%).

– Solar Panels contributed with 90% while the wind turbine contributed with 10% to the RE mix.

– Due to the governmental subsidies of electricity, non of the PV or WT was found feasible .

– The solar panel is more feasible than the wind turbine.

– Modify the mechanism, so the grid cover only the energy shortage due insufficient supply from renewable and storage systems.

– Regular maintenance and upgrading the data acquisition system.

– Subsidies should be provided to RE technologies for them to be feasible .

– Optimization of the station operation/configuration, for further CO2 savings.

– Bapco, Awali Services

– Prof Waheeb Al-Naser & Mr Hussain Al-Ansari

– Heliocentris (system manufacturer)

– Our students: Ali Salman, Hussain A.Ali, Alaa Jameel.

[1] Dr.Cluas Fischer and Dr. Nroman Siehl, 2010, Heliocentris Energirsysteme GmbH, Power Point presentation, Heliocentis, Berlin-Germany

[2] Montgomery etal, (2008). Introduction to Time Series Analysis and Forecasting, Wiley. River Street Hoboken

[3] Description of Transformation, http://www.xlstat.com/en/learning-center/tutorials/using-

differencing-to-obtain-a-stationary-time-series.html, [Last visit on 13 January 2013]

[4] The Royal Academy of Engineering (Wind Turbine Power calculations)

http://www.raeng.org.uk/education/diploma/maths/pdf/exemplars_advanced/23_wind_turbine.

pdf, [Last visit on 6 January 2013]

[5] Alfasolar, (Solar Module Series alfasolar pyramid 54) , http://www.alfasolar.de, [Last visit on 6 January

2013]

[6] EERE Information Center-U.S (Fuel Cell Technologies Program)

http://www1.eere.energy.gov/hydrogenandfuelcells/pdfs/fct_h2_fuelcell_factsheet.p

df, [Last visit on 6 January 2013]